Copyright © 2019 by Jon D Markman All rights reserved Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher ISBN: 978-1-26-013222-9 MHID: 1-26-013222-6 The material in this eBook also appears in the print version of this title: ISBN: 978-1-26-013221-2, MHID: 1-26-013221-8 eBook conversion by codeMantra Version 1.0 All trademarks are trademarks of their respective owners Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark Where such designations appear in this book, they have been printed with initial caps McGraw-Hill Education eBooks are available at special quantity discounts to use as premiums and sales promotions or for use in corporate training programs To contact a representative, please visit the Contact Us page at www.mhprofessional.com This publication is designed to provide accurate and authoritative information in regard to the subject matter covered It is sold with the understanding that neither the author nor the publisher is engaged in rendering legal, accounting, securities trading, or other professional services If legal advice or other expert assistance is required, the services of a competent professional person should be sought —From a Declaration of Principles Jointly Adopted by a Committee of the American Bar Association and a Committee of Publishers and Associations TERMS OF USE This is a copyrighted work and McGraw-Hill Education and its licensors reserve all rights in and to the work Use of this work is subject to these terms Except as permitted under the Copyright Act of 1976 and the right to store and retrieve one copy of the work, you may not decompile, disassemble, reverse engineer, reproduce, modify, create derivative works based upon, transmit, distribute, disseminate, sell, publish or sublicense the work or any part of it without McGraw-Hill Education’s prior consent You may use the work for your own noncommercial and personal use; any other use of the work is strictly prohibited Your right to use the work may be terminated if you fail to comply with these terms THE WORK IS PROVIDED “AS IS.” McGRAW-HILL EDUCATION AND ITS LICENSORS MAKE NO GUARANTEES OR WARRANTIES AS TO THE ACCURACY, ADEQUACY OR COMPLETENESS OF OR RESULTS TO BE OBTAINED FROM USING THE WORK, INCLUDING ANY INFORMATION THAT CAN BE ACCESSED THROUGH THE WORK VIA HYPERLINK OR OTHERWISE, AND EXPRESSLY DISCLAIM ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE McGraw-Hill Education and its licensors not warrant or guarantee that the functions contained in the work will meet your requirements or that its operation will be uninterrupted or error free Neither McGraw-Hill Education nor its licensors shall be liable to you or anyone else for any inaccuracy, error or omission, regardless of cause, in the work or for any damages resulting therefrom McGraw-Hill Education has no responsibility for the content of any information accessed through the work Under no circumstances shall McGraw-Hill Education and/or its licensors be liable for any indirect, incidental, special, punitive, consequential or similar damages that result from the use of or inability to use the work, even if any of them has been advised of the possibility of such damages This limitation of liability shall apply to any claim or cause whatsoever whether such claim or cause arises in contract, tort or otherwise I dedicate this book to my children, Joseph and Janie, who will see many of the exciting technologies described in this book move from the fringe to the norm, and will help shape the next generation’s view of the future I would also like to acknowledge the contributions of my excellent researcher, TB, and my wife, Ellen CONTENTS Introduction CHAPTER 1: Cloud Computing: The New Electricity CHAPTER 2: Sensors: Analog Becomes Digital CHAPTER 3: Decoding the Genome: Stretching the Meaning of Life CHAPTER 4: Big Data: Making Sense of It All CHAPTER 5: Predictive Analytics: The End of Hit or Miss CHAPTER 6: Artificial Intelligence: Computing Evolves CHAPTER 7: Robotics: Rise of the Machines CHAPTER 8: Blockchain: The Transparency Revolution CHAPTER 9: Self-Driving Cars: The Ultimate Paradigm Shift CHAPTER 10: The Internet of Things: Smart Networks Everywhere CHAPTER 11: Gene Editing: Reshuffling the Building Blocks of Life CHAPTER 12: Precision, Nano, and Regenerative Medicine: Science Fiction Meets Reality Index INTRODUCTION T he world is accelerating at an exponentially brisk pace toward a future in which cars drive themselves, software writes itself, faulty human genetic code edits itself, and the computing power helping all of this happen is virtually limitless It is an era when fast forward is not just a button on a remote control; it is a description and an aspiration for entrepreneurs, workers, government officials, programmers, and physicians The stretch of time that lies ahead has the potential to create the greatest economic boom that the world has ever known, surpassing the periods that featured the discovery of fire, the discovery of electricity, the magic of flight, or the invention of computers themselves Every impressive technology we have seen up to now has just been a prelude Fast forward: It is a dream and reality at the same time, as yesterday’s science fiction becomes toys for children today, and crazy ideas like robotic trucks, Mach ground transportation, drone armies, and highly intelligent and adaptive home furnishings are becoming a plausible reality All of this is going to make entrepreneurs and their backers very wealthy, but public shareholders will benefit too, and grandly And that’s the subject of this book This book chronicles how all of the building blocks are coming together, each augmenting the previous ones, enabling visionary entrepreneurs to build truly transformational businesses I will show how powerful cloud networks democratized supercomputing, leading to groundbreaking changes in genomics and artificial intelligence I will show how the rise of inexpensive sensors helped researchers turn the physical world into ethereal digital bits, and how information scientists are harnessing that data to build revolutionary software that is changing how products are built, and how services are delivered Also I will show the applications that are possible when all of these blocks come together Get ready for self-driving cars, gene editing, and advances in life sciences that will bring microscopic robots, human organs generated in labs, and medical treatments tailored to our specific genomics Most important, along the way, I will show you how to take advantage I’m going to show you which trends are important, and which you should ignore I’m going to lay out the fast forward movers and shakers, the companies building integral platforms and competitive advantages that will be difficult to reproduce In short, I am going to show you which companies are most likely to make investors rich as they hurdle to the very edges of practice and possibility CHAPTER CLOUD COMPUTING: THE NEW ELECTRICITY I n the late 1800s, the proliferation of cheap industrial electricity changed commerce It led to vibrant new ecosystems that fostered further innovation Cloud computing is serving the same role today It is transformational In this chapter I will show how Amazon.com founder Jeff Bezos created this new era with a stroke of rare insight, carving a path for a new generation of entrepreneurs to follow You will also learn how two other entrepreneurs—Mark Zuckerberg of Facebook and Reed Hastings of Netflix—would cleverly leverage cloud computing to become legends in their own right And you will see how companies are still racing to move their business to the cloud two decades after these pioneers lit the pathway But first, for valuable context, I want you to take a quick detour into history to learn how an underappreciated giant of nineteenth-century business set the tone for today’s innovations by disrupting industry with the development of mass-market electricity Henry Burden Henry Burden, the son of a Scottish sheep farmer, landed in upstate New York in 1819 after studying engineering at the University of Edinburgh Dead set on making his fortune in the burgeoning American industrial complex, by 1835 he had patented machines to forge the spikes used for the railroad industry He invented another machine that made horseshoes His company, Burden Iron Works, astounded competitors by making 60 a minute Ultimately, that prowess allowed Burden to supply the Union Army during the Civil War At the time, machine-made horseshoes were sold in 100-pound kegs Burden sold 600,000 kegs annually, generating $2 million in sales That’s $55.4 million in 2018 dollars—serious business Like so many industrialists of his era, such as fellow Scottish émigré Andrew Carnegie, Burden understood that ubiquitous, cost-effective power was critical to the prosperity of his business So in 1851, he designed a massive, on-site power generation utility The Burden Water Wheel rose 60 feet out of Wynantskill Creek in upstate New York The enormous steel structure was the most powerful vertical waterwheel in history It powered two large ironworks facilities that employed hundreds of men Puddling and heating furnaces, rivet and horseshoe machines, rotary squeezers, steam engines, and boilers were powered by the great wheel Inspired by this invention, all across the country industrial sites began popping up alongside rivers Access to affordable and abundant power, generated by waterwheels, was the primary consideration Three decades later, George Westinghouse took power generation to the next level The gifted young New York inventor used Siemens alternators and his keen business wits in the 1880s to figure out how to distribute affordable alternating current electricity long distances through wires to industrial sites far from waterways Over the course of the next twenty years, business went all in As the price of electricity fell, the market share for waterwheel-based power plunged from 100 percent to just percent Although Burden’s waterwheel became obsolete, the precedent he set lived on Inexpensive electricity transformed the world Working solely in his own self-interest, he inadvertently brought power to the people in the same way that the cloud would bring computing to the people in our era Jeff Bezos When Amazon.com founder Jeff Bezos sat down with 60 Minutes for the first time in 1999, the online retailer was already a phenomenon Its product line had swollen from books to CDs and DVDs Customers and sales had grown exponentially Yet when asked about potential growth ahead for the company, Bezos demurred He conceded the young industry was in a category formation period, when potential was enormous and uncountable He sandbagged the interviewer and competitors in an effort to gain a psychological advantage, but even then he saw the bigger picture He was already building out a network of cloud-based computer systems Still, he could not have known then that his fledgling Seattle online store was laying the foundation for the most significant age of invention the world has ever known He could not have known that unprecedented wealth lay ahead—not just for him but for shareholders and thousands of entrepreneurs who would careen crazily forward on his copious coattails Like so many successful entrepreneurs, Bezos is razor-sharp, driven, and eccentric As a young man, he parlayed his love for mathematics and bright mind into a high-paying job as a quantitative investment analyst on Wall Street The Princeton graduate founded Amazon.com in 1994 after leaving the hedge fund D.E Shaw Many years later, he would admit that starting an online bookstore then was a risk best taken by someone with less to lose Still, he had fired up his car and moved west to Seattle, determined to not live the remainder of his life wondering what might have been To seed the company, he rounded up 20 investors at $50,000 apiece That $1 million bought them a 20 percent stake in a big idea Even by angel investor standards, the valuation was steep But Bezos, ever the numbers guy, would not relent He sold early investors on the idea that a virtual storefront offered unprecedented leverage According to his models, an average online store should 27 times as much business as a comparable brick and mortar storefront His math, or at least his sales pitch, resonated When the company went public in 1997, annual sales were just $15.7 million After the initial public offering, flush with cash, Bezos began positioning for the future In his original 1997 letter to shareholders, he wrote about what was essential to the new enterprise He promised to prioritize customer service and sales growth over profitability because scale was primary to achieving the business model objectives at Amazon.com He vowed to build shareholder value by focusing relentlessly on customer satisfaction He pledged a lasting commitment to the three guiding principles of low prices, vast selection, and fast delivery And he promised, above all else, to prioritize long-term growth over short-term rewards Under the microscope of Wall Street analysts, the ability to defer gratification is often impossible, even for established companies Amazon.com was all of a year old as a public firm But it was clear: Bezos was building a business that could scale It was a wise decision By 2003, annual sales had rocketed to $5.23 billion Four years later, a decade after the 1997 shareholder manifesto, annual sales had risen almost tenfold to $14.84 billion Throughout this exciting period, Bezos stayed true to his word The company continued to make aggressive long-term investments, often at the expense of profitability The company leased warehouses It hired managers and workers at a breakneck pace However, the most significant investment was devoted to digital infrastructure Amazon.com built massive data centers, filled with expensive servers that ran custom software Customers always took for granted that their personal information and order history was collected and safely stored Beneath the surface, the combination of digital infrastructure and data analytics was doing much more It was funneling reams of structured data into a large knowledge engine and making surprisingly accurate guesses about other items patrons might like to buy on the site Who knew buyers of Ian McEwan’s novel The Comfort of Strangers might also be pop singer Elvis Costello fans? It was running complex cyber security And it was plugging into a network of thousands of remote servers that were storing, managing, and processing data at previously unimagined speed The idea of networked computers was not new The Internet itself is a network, and in those early years of dot-com mania, it had captured investors’ attention the way cryptocurrencies did two decades later What was different about the Amazon.com experiment was scale and application Decisive action was required to safeguard its e-commerce platform from hackers and provide computing power to make everything run smoothly The company had to reimagine the network It became a massive new internal utility Amazon Web Services included large data centers, strategically located all over the world Collectively, tens of thousands of networked servers hummed 24/7 And all of this computing power was virtualized through custom-built Internet connections Then in 2002, Bezos changed everything He sent an interoffice memo to the web services teams The directive ordered crews to begin communicating through open application programming interfaces only There were to be no other forms of communication No shared direct linking No shared memory models No back doors whatsoever All teams were to expose their work and design interfaces as though they were visible by outside developers In other words, software engineers were to begin coding with application programming interfaces, or APIs, as though all of their work was available to external developers In typical Bezos fashion, the memo ended with, “Anyone who doesn’t this will be fired Thank you: have a nice day!” From that point, Amazon Web Services (AWS) became a service-oriented architecture It also became a platform Company evangelists started encouraging outside developers to write modular applications that could be plugged into the secure platform The sheer size and utility of the experiment changed information technology infrastructure Computing power, storage, and security became ubiquitous By 2006, AWS boasted a community 150,000 strong Later that year, AWS began selling its spare computing power and storage to developers, researchers, governments, and enterprises on a pay-as-you-go basis Suddenly, anyone with a big idea and a credit card had access to a virtual supercomputer The combination was powerful It was like electricity It allowed smart kids in garages and college dormitories to invent new stuff that would have otherwise been pipe dreams It helped established companies reinvent their business models And it helped researchers and academics better understand complexities that had been mysteries I put my own business on AWS in 2005 and never looked back in camera technology buyout, 35 connected home and, 210–212 graphic processing units (GPUs), 111–115, 178 Griffin, Liam, 181 GTC Biotherapeutics, 229 Guidant, 143 Guidewire Software, 111 hackers: banks attacked by, 159–160 blockchain and, 149–150, 151, 169 Internet of Things and, 212–215 Hadoop, 64–65, 220 Hall, Martin, 86 Hammerbacker, Jeffrey, 250 hardware: in artificial intelligence applications, 86, 117, 122 legacy, data available on, 75 sensors used in developing, 26 Harris, Parker, 95 Hassabis, Demis, 116, 118 Hastings, Reed, 1, 13, 15–17, 23, 91 Hawking, Stephen, 103, 117 healthcare: cost of, 67, 260 digitalization in, 265–266 personalized, 244–246 predictive analytics use in, 99 regenerative medicine in, 87–89 smart technology and, 203–204, 209–210, 259–261 Healthcare.gov, 265 Healthy.io, 259, 260 hedge funds, 73–75 Heppelmann, James, 97 hive mind, 198 home automation, 120 Homology Medicines, 239–242 Honeywell, 202 Hsing, Michael, 39–40 Huang, Jen-Hsun (“Jensen”), 112, 114, 132–133 Huang, William Wei, 79 Huawei, 72, 80 Human Genome Project (HGP), 44–47, 57, 59, 60, 224–226, 229, 244, 245 Human Genome Project-write, 226 Human Longevity Inc (HLI), 47–49 Humans Need Not Apply (Kaplan), 124 IAM Robotics, 133–134 IBM, 85–86, 125, 150, 154, 155, 200 ID2020 digital identity program, 157 Illumina Inc., 57–60, 236, 238, 242, 246 imaging technology, 37–39 Immelt, Jeffrey, 71 immunotherapy, 246–251 IMS, 56 information technology: commodification of, 33 global power of, mass-market sale of, 7–8, 17, 20 in New Gilded Age, 136 rapid evolution of, 103, 177, 179, 181, 229 Innovative Health Solutions, 263 Instagram, 12 insurance industry: artificial intelligence in, 108–111 robotics in, 125 (See also auto insurance) integrated circuits (ICs), 39–41, 194 Intel, 84, 86, 103, 195, 196 intellectual property (IP), 34, 35 Internet: advertising on, 119–121, 122 blockchain and, 157–158 content streaming on, 13, 16 data collected from, 75 data processing and, 64 golden era of, 166 number of devices connected to, 51 search engine for, 118–119 Internet of Things (IoT): Alexa and, 206–207 applications for, 199–200 China and, 81 convergence and, 197, 208 function of, 198 growth of, 145, 203–204 in healthcare, 209–210 household, 210–212 industrial, 72, 216–217 investing in, 198, 215, 218, 220, 222, 223 package delivery and, 207 platforms for, 168, 212, 218–221 predictive analytics for, 96–98 security of, 212–215 (See also connectivity; networked technology; smart technology) Intuitive Surgical, 127, 143–145, 147 investment: in addiction treatment, 263 in agricultural technology, 32, 33 in artificial intelligence, 105, 107, 113–115, 118, 121, 122 in automotive services, 186 in automotive technology, 37, 39, 187, 193 autonomous driving and, 176, 188, 191, 192, 195, 196 in big data, 82, 222, 223 in biotechnology, 45–46, 58–60, 227–241 in bitcoin, 153 in blockchain platforms, 156, 158, 169, 170 in cloud technology, 19, 21, 23, 221 in commodities, 34–35, 43 in customer relationship management software, 96 in digital payment firms, 185 in 5G networks, 182 in gene technology, 224, 227, 231–232, 235–238, 240–242 in healthcare digitalization, 266–267 in hedge funds, 74–75 in immunotherapy, 251 in insurance claim technology, 110, 111 in integrated circuits, 42 in Internet of Things, 198, 215, 218, 220, 222, 223 in mining technology, 30 in nanotechnology, 256, 259 in niche markets, 35, 147 in optical technology for robots, 36, 37 in pharmaceutical research, 45–46, 263 in predictive analytics, 98, 99 in regenerative medicine, 89 in robotics, 36, 37, 125–126, 132, 133, 138, 141, 147 in robotics for surgery, 142–143, 145 in semiconductors, 111 in surveillance technology, 107 IQVIA Holdings, 55–57, 60 J Craig Venter Institute, 226 Jochem, Todd, 172 John Bean Technologies (JBT), 145–147 Johns Hopkins, 129, 130 Johnson, Brian, 189–190 Johnson & Johnson, 200, 251, 259 Johnson Controls, 199 Jonas, Adam, 173–175 J.P Morgan (bank), 160 June, Carl, 247 Juno Therapeutics, 250 just-in-time manufacturing, 137–138 Kaiser Permanente, 68 Kamen, Dean, 87–89, 99 Kaplan, Jerry, 124 Kite Pharmaceuticals, 250 Kiva, 126, 201, 202 Kline, Terry, 221 Komatsu Mining Corp., 29 Krebs, Brian, 213–215 KrebsOnSecurity, 213–215 Krishna, Arvind, 154 Kroo, Elliot, 183 Kshatriya, Ajay, 54 Kurzweil, Ray, 254–256 language translation, 17, 102 Lemonade Insurance Co., 108–109 Lerner, Sandy, 166 Levinson, Arthur, 48 Liberty Mutual, 109 LiDAR, 179–180 Ling Ting Ming, 107 Lo, Andrew, 74 Lockheed Martin, 105 Loladze, Irakli, 232–233 longevity, human, 47–49, 59 Luminar, 179–180 Lundbaek, Leif-Nissen, 163 Lyft, 186, 193, 207 Ma, Jack, 135 machine learning, 84, 101, 109–111, 115, 119, 121, 122 Maersk, 154–155 Malhortra, Neil, 128 malware, 160, 214 manufacturing: big data in, 71–73 Internet of Things and, 97, 205, 207, 223 predictive analytics in, 199 robot use in, 126, 134–141, 145, 148 maps, 3D, 179, 181 Marsh & McLennan, 154 Masayoshi Son, 32 Matheny, Johnny, 129–131 Matternet, 207–208 Mazor Robotics, 131, 141–142, 147 McLaren, Robert, 49–50 Mechantronics, 139 Medallion hedge fund, 73–74 medical records, digital, 69–70 medicine: bioelectronics in, 251–254 epidemics and, 236–237 nanotechnology in, 256–259 pharming and, 227–229 precision, 57–60, 69, 118, 244–246 robotics in, 127–131, 132, 141–145, 251–254 T-cell therapy in, 88, 246–251 technology convergence with, 243–244, 265-266 telecommunication in, 181 (See also healthcare) Medtronic, 141–142, 200 Mercer, Robert, 74 Messenger, 12 micro-electro-mechanical systems (MEMS), 104 Microsoft: Azure, 114, 157, 164–166 blockchain and, 156–158, 161, 165, 166, 170 cloud computing and, 164–166 deep learning research by, 112 investing in, 53, 166 nanotechnology research by, 18 synthetic DNA research by, 50–52 mining technology, 28–30, 43 mobility businesses, 173–174, 184, 196 Modular Prosthetic Limb (MPL), 130–131 Moll, Frederic, 143–144 Monolithic Power Systems, 39–43 Monsanto, 230, 231, 239 Montgomery, Will, 161, 162 Moret, Blake, 217 Morgan, J.P (banker), 34 Morgan Stanley, 173, 174, 176 Mornhinweg, Volker, 207 Motoman robots, 139–140 Mucic, Luka, 219 Muir, William, 228 Musk, Elon, 103, 117, 136–138 Nadella, Satya, 164 Nakamoto, Satoshi, 152, 153 nanotechnology, 18, 254–259 National Aeronautics and Space Administration (NASA), 26, 27 National Cancer Institute, 245 National Institutes of Health, 45, 245 Navistar, 221 NBASE-T alliance, 194 Nest, 210–211 Netflix: business model of, 13–14, 16 edge computing and, 167 growth of, 1, 12–16 investing in, 176, 177 as leading cloud business, 19, 177 predictive analytics used by, 91–93, 99 Netscape, 66–67 networked technology: in businesses, 96–98, 203 for cars, 166, 181 cyber security and, 150–151, 164 in data processing, 64–65, 167 growth of, 200 in manufacturing, 205, 216 platform for, 168–169 power modules for, 40, 41 for shopping, 205 for smart cities, 98 (See also connectivity; Internet of Things; smart technology) networks, 5G, 98, 172, 180–182, 189 neural networks, 102, 112, 113, 116, 117, 178 Neuro-Stim System-2 (NSS-2) Bridge, 261–263 Ng, Andrew, 127 Northrup Grumman, 105 Novartis, 88, 236, 240, 242, 248 nuTonomy, 188, 192, 193 Nvidia: AI supercomputer from, 187–188 autonomous vehicles and, 167, 177, 191, 195 connectivity at, 219 deep learning technology and, 111–115, 122 investing in, 178, 191, 196 robotics at, 132 Oettinger, Günther, 181 O’Malley, Bert W., Jr., 128 Opiant Pharmaceuticals Inc., 263 opioid addiction, 244, 261–264 Opower, 221 optical character recognition (OCR), 35, 36 Oracle, 96, 114 Ottomatika, 192 Page, Larry, 118–119, 184 PageRank, 119 Palo Alto Networks, 77 Parker, Sean, 248, 250 Parker Institute for Cancer Immunotherapy, 250 Parrish, Elizabeth, 48 pattern recognition: artificial intelligence and, 101 in big data, 65–68, 71, 82, 85 in hedge fund investing, 73–75 in real-time data, 98 Pernikoff, Noah, 127–129 personalization: of media content, 13–16, 91–93 in medicine, 118, 244–246 in shopping, 205 Pfizer, 259 pharmaceutical industry: artificial intelligence in, 118 clinical trials in, 55–57, 59 genetics-based information technology in, 55–59, 240 immunotherapy and, 250 investing in, 45–46, 263 nanotechnology in, 256, 257 precision medicine and, 245–246 pharming, 224, 227–229 Philips, 209, 210 Planet Labs, 26–28 Plenty, 32–33 Pole, Andrew, 62, 63, 75, 82 Pomerleau, Dean, 172 Porsche, 162–163 precision medicine, 57–60, 69, 118, 244–246 predictive analytics: in biotechnology, 89, 91 impact on business, 85–87 investing in, 99 in manufacturing, 199 personalization and, 91–93 product development using, 83–84, 96–97 in surgical robotics, 144 Predix, 71, 72, 199 Priceline.com, 66 printed circuit boards (PCBs), 31 product development, 83–84, 96-97 productivity: connectivity and, 217, 223 data collection and, 135 employment and, 203 improving, 136 predictive analytics and, 199 robotics and, 136–138, 145 wage growth and, 126 Product Lifecycle Management (PLM), 96–97 Project Shield, 214 Proofpoint, 77 PTC Inc., 96–99 Qualcomm, 103, 180, 182, 189, 196, 208, 209 Randolph, Marc, 13, 91 Raytheon, 105 ReachNow, 186 regenerative medicine, 87–89 Regenesis: How Synthetic Biology Will Reinvent Nature and Ourselves (Church), 90 Renaissance Technologies, 73–75 Resolution Copper Mining, 28, 29 retailers: data collection by, 61, 75 robot use by, 148 Rio Tinto PLC, 28, 30, 43 robots: in automotive applications, 36, 205 component manufacture for, 35 in food processing, 145–147 functionality of, 131–133 implantable, 18 improvements in, 123, 124 industrial, 36, 134–141, 148 investing in, 36, 37, 125–126, 132, 133, 138, 141–143, 145, 147 job security and, 124–127, 148, 202, 203 in medical applications, 127–131, 141–145, 181, 251–254 in mining, 29, 30 motor technology for, 41 nano-scale, 258 in police work, 106–107 productivity and, 136–138, 145, 146 software for, 125, 132, 133, 148, 192 taxi service by, 188, 190, 193 in warehouses, 201 (See also bots) Rockefeller, John D., 34, 43 Rockwell Automation, 205, 215–218 Rometty, Ginni, 85–86 Rothblatt, Martine, 48 Rubin, Andy, 120 Ruh, Bill, 71, 72 Russell, Austin, 180 SAB Biotherapeutics, 228 Salesforce.com, 19, 57, 85, 93–96, 99 Samsung, 30, 34, 35, 212 SAP SE, 218–220 Sarandos, Ted, 14, 92 Sasson, Steve, 25, 26 satellite technology, 26–28, 43 Schingler, Robbie, 27 Schmidt, Eric, 32 Schneier, Bruce, 212–213 Schreiber, Daniel, 108 Scorpio, Jessica, 183, 184 Sedol, Lee, 116 self-driving cars (see autonomous vehicles) self-service technology, 42 semiconductors, 40–41, 111, 198 sensors: in agriculture, 32–33 in automotive products, 36 in cameras, 34 for connected devices, 146, 199 in contact lenses, 30–31 cost-effective production of, 24, 28, 42–43, 204 data collected by, 51, 75, 198, 220 functions of, 25–26, 43 investing in, 43 in mining, 28–30 in precision medicine, 245 rapid development of, 25 in robotics, 36, 124, 130, 140, 148 in satellites, 27 in smartphones, 25, 26 in thermal imaging technology, 37 in virtualization technology, 71 Seton Healthcare, 86 shale production, 53–55 sharing economy, 186 Sharkey, Noel, 103 Shillman, Robert, 35 shipping industry, 154–156, 219 Shoham, Moshe, 141 Simons, James, 73, 74 Singapore: blockchain and, 165 taxibots in, 188, 193 Skynet, 79 Skyworks Solutions, 181 Slaoui, Moncef, 254 Slaughterbots (film), 104, 105 Slock.it, 169 smart cities, 17, 75, 98, 165 smart contracts, 153–154 smartphones: blockchain transactions and, 169 in car-sharing services, 184 data collected from, 75, 167 data storage on, 17 Facebook use on, 11–12 financial chatbot app for, 84–85 in healthcare, 69, 259–261 increased network speed and, 180 for insurance claims, 109 MEMS in, 104 profitability of, 120 proliferation of, 24, 182 safe driving app for, 110 sensors in, 25, 26 in shopping, 205 subscription service for, 196 SmartSignal, 72 smart technology: blockchain use by, 151 in cars, 75 economic impact of, 203–204 in factories, 35, 146 for robots, 140 Society for Worldwide Interbank Financial Telecommunication (SWIFT), 159, 160, 162 software: for artificial intelligence, 114 for autonomous vehicles, 192, 193 for big data analysis, 64, 66, 67, 219–222 for blockchain, 153–154 for connected home technology, 211–212 connectivity and, 40, 198, 218–219 for customer relationship management, 94 for cyber security, 77, 160–162 diverse applications for, 17 for drone swarms, 103 in insurance industry, 109, 111 Internet-based, 96 open-source, 64, 78, 152, 155, 165, 220, 222 predictive analytics, 84, 86, 91 for robots, 125, 132, 133, 148, 192 as transformative, 261 virtualization, 71, 75 Sony, 30, 31, 34 Spamhaus, 215 SpineAssist, 141 Splunk, 76–78, 82 Spotify, 18, 186 Stanford Research Institute (SRI), 143 stem cells, 48, 88 streaming: of media content, 13–16, 177 of real-time data, 86 subscription services: for cars, 185–187, 196 for hacking, 214 vs one-time sales, 21, 78 supply chain: in artificial intelligence technology, 105 in automobile industry, 194–195 blockchain use in, 154–156 connectivity and, 216–217, 223 in food production, 33 in healthcare, 70 inventory management in, 204 in surveillance technology, 106 for US Air Force, 97 surgery: nanotechnology in, 256 robotics use in, 127–132, 141–145, 181 surveillance: in China, 79–81 drone technology for, 103, 104 imaging technology for, 37 ubiquitous, 105–107, 121 Swan, Erik, 76 Swift Solution Suite (IAM Robotics), 133–134 Symantec, 77 Target Corp., 62–63, 204–205 Tas, Jeroen, 210 Taylor, Paul, 161, 162 T-cell therapy, 246–251 TEDMED Foundation, 65 Teleflex Inc., 227 telemedicine, 181 Tencent, 80, 114 terrorism: biological, 236–238 smart-drones and, 103 surveillance technology and, 107 Tesla, 113, 136, 137, 178 thermal imaging technology, 37–39 ThingWorx Analytics, 97, 98 Thought Machine, 161–162 T-Mobile, 86 Tour, James, 257, 258 Toyota, 137, 166, 173, 183, 184, 207 transgenic animals, 227–229 23andMe, 259 Twist Bioscience, 52 Uber, 18, 173, 184, 186, 191, 207 Umpleby, Jim, 80 Under Armour, 200 Unified Data Architecture (UDA), 69–70 UnitedHealth Group (UNH), 59 United Parcel Service, 203 United Therapeutics, 48 US Air Force, 97, 103–104 US Coast Guard, 97 US Department of Defense (DOD): cloud services used by, 20, 21 DARPA, 31, 124 electronic health records for, 265 regenerative medicine and, 89 robotic prosthetics use by, 130 surveillance drone technology used by, 104 US Department of Energy, 45 US Food and Drug Administration, 60, 245, 261 US Navy, 104 US State Department, 20 Vault OS, 161 vDOS, 214 Velodyne, 180 Venter, Craig, 45, 47, 59, 90, 235 Verily, 18, 249–254, 260 vertical farming, 32–33, 43 Video Surveillance-as-a-Service (VSaaS), 106, 107 virtualization: computer hardware and, 166–167 for corporate datasets, 76 industrial applications for, 71, 72 medical applications for, 127–128, 144 robot functionality and, 132 visible light communication (VLC), 205 Vodafone, 98 Volkswagen, 173, 207 Volvo, 185–187 Walker, Jay, 65–66 Walmart, 155, 203, 204 Wan, J C., 53 Watson Explorer software (IBM), 125 Watson Health Cloud (IBM), 200 Waymo, 179, 187 Wells Fargo, 160 Westinghouse, George, 3, 9, 17 WhatsApp, 12, 126–127 Whitehead, Emily, 246–248 Whole Foods, 21, 22 Wilby, Alvin, 103 wireless technology: for autonomous vehicles, 172 in drone navigation, 208 increasing speed of, 180–183, 189 integrated circuits used in, 40, 41 in smart cities, 98 Wise.io, 72 Wladawsky-Berger, Irving, 157 XAIN, 162–163 Yahoo!, 77, 119 Yaskawa Electric Corp., 135, 139–141, 147 Yasukawa, Keiichiro, 139 Younge, Robert, 143 Zaid, Sam, 183, 184 Zehavi, Eli, 141 Zendrive, 110 Zipcar, 184 Ziska, Lewis, 233, 241 Zuckerberg, Mark, 1, 10–11, 12, 23 ... along the way, I will show you how to take advantage I’m going to show you which trends are important, and which you should ignore I’m going to lay out the fast forward movers and shakers, the companies... programming tailored to its target audience The company knows its customers It understands what they want and how to deliver The flexibility of the cloud allows Netflix to bolt on software to run the business... size of a toaster Its data was recorded on cassette tape The only way to view the grainy, blackand-white image was on a TV He took the prototype to the Kodak board of directors Young and ambitious,